Magnetic resonance (MR) imaging (MRI) is routinely used to diagnose prostate cancer (PCa) and identify the stage of PCa. PCa may induce changes in the shape of the prostate capsule and central gland (CG) in biopsy positive (Bx+) patients relative to biopsy negative (Bx−) patients, elevated-prostate specific antigen (PSA) patients, or normal patients. PCa may also induce changes in the volume of the prostate and CG in Bx+ patients relative to Bx− patients, elevated-PSA patients, and normal patients. These changes in the shape and volume of the prostate may be observed in T2 weighted (T2w) MRI images.
Radiation therapy and radical prostatectomy are common treatments for PCa, with over 50% of PCa patients being treated with either or both treatments. However, radiation therapy has a failure rate as high as 25%, and 30-35% of treated PCa patients experience treatment failure within ten years. Predicting biochemical recurrence (BCR) prior to treatment may enable better planning and personalization of treatment. MR images may be used to assist the prediction of BCR in PCa patients. However, when obvious extra-capsular spread of the disease is not present, conventional approaches employing MRI are not useful for distinguishing patients who will experience BCR from those who will not.
Multi-parametric MRI (mpMRI) is widely used in the management of PCa to improve the detection, tumor staging, and risk stratification for selection of patients for active surveillance and recurrence prediction of the disease. Despite its broad adoption in the management of PCa, conventional approaches for predicting BCR using MRI, including mpMRI, are susceptible to variability in MRI acquisition parameters, scanner protocols, image artifacts, and non-standardized image intensities. This variability may occur both within an individual institution (e.g., hospital, university) and across multiple institutions. Conventional approaches to MRI-based PCa diagnosis, identification, or prognosis prediction may employ protocols or guidelines for imaging acquisition parameters and findings reporting, although score interpretation and detection thresholds, particularly across multiple institutions, have not been uniformly applied or exhaustively studied. Furthermore, implementing protocols and guidelines across different institutions takes time, costs money, and puts a patient at additional risk if the guidelines and protocols are not consistently applied.
Radiomics or computer extracted texture features have been used to characterize tumor presence on MRI images. Radiomics-based approaches quantify sub-visual patterns represented in radiological images, including MRI images. However, texture analysis does not always allow for discrimination between more and less aggressive disease. Conventional radiomics-based approaches may also suffer reduced BCR predictive accuracy caused by inter-protocol misalignment, image acquisition artifacts and non-standardized image intensities.